(Ebook PDF) Foundations of Calculus for Data Science An Foundational Guide to Understanding the Application of Calculus to Data Science 1st edition by Hayden Van Der Post, Vincent Bisette 979-8875973451 full chapters

Foundations of Calculus for Data Science: An Foundational Guide to Understanding the Application of Calculus to Data Science 1st edition by Hayden Van Der Post, Vincent Bisette – Ebook PDF Instant Download/DeliveryISBN:  979-8875973451 

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Product details:

ISBN-13 : 979-8875973451

Author: Hayden Van Der Post, Vincent Bisette

Reactive Publishing

“Foundations of Calculus for Data Science” is an innovative book that bridges the gap between abstract mathematical concepts and their practical applications in the burgeoning field of data science. This book is a must-have for anyone aspiring to excel in data science, analytics, or any field where data is a key player.

Expertly crafted for both beginners and those looking to deepen their understanding, the book starts with the basics of calculus, making it accessible even if you’ve never encountered calculus before. It then builds up to more complex concepts, ensuring a solid grasp of the fundamentals.

What sets this book apart is its focus on real-world applications. Each calculus principle is linked to data science, offering a practical perspective that is often missing in traditional calculus textbooks. You’ll explore how differential and integral calculus are used in optimizing algorithms, machine learning models, and big data analysis, equipping you with the mathematical tools essential for success in these fields.

 

Foundations of Calculus for Data Science: An Foundational Guide to Understanding the Application of Calculus to Data Science 1st Table of contents:

Mathematics as the Backbone of Data Science and Machine Learning

1. Data Representation and Transformation
2. Statistical Analysis and Probability
3. Optimization Techniques

Key Mathematical Disciplines in Data Science and Machine Learning

1. Linear Algebra
2. Calculus
3. Probability and Statistics
4. Discrete Mathematics

Mathematics for Generative AI

1. Linear Algebra and Vector Calculus
2. Probability and Information Theory
3. Optimization and Game Theory
4. Transformers and Sequence Models

 

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